Instructions to use argv947059/example-based-ner-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use argv947059/example-based-ner-bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="argv947059/example-based-ner-bert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("argv947059/example-based-ner-bert") model = AutoModel.from_pretrained("argv947059/example-based-ner-bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from argv947059/example-based-ner-bert: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/argv947059/example-based-ner-bert/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://argv947059/example-based-ner-bert/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/argv947059/example-based-ner-bert/resolve/main/flax_model.msgpack
438 MB
- Xet hash:
- a86447c789ecf5e8cf056151cef618179291ddab89cff59d86f1c9435da81438
- Size of remote file:
- 438 MB
- SHA256:
- 0470b56ab7a0c3fb08a62bab98e53ce56892b91760692e19772b2eadd790d2de
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